Energy-Effective Data Gathering for UAV-Aided Wireless Sensor Networks

Unmanned aerial vehicles (UAVs) are capable of serving as a data collector for wireless sensor networks (WSNs). In this paper, we investigate an energy-effective data gathering approach in UAV-aided WSNs, where each sensor node (SN) dynamically chooses the transmission modes, i.e., (1) waiting, (2) conventional sink node transmission, (3) uploading to UAV, to transmit sensory data within a given time. By jointly considering the SN’s transmission policy and UAV trajectory optimization, we aim to minimize the transmission energy consumption of the SNs and ensure all sensory data completed collected within the given time. We take a two-step iterative approach and decouple the SN’s transmission design and UAV trajectory optimization process. First, we design the optimal SNs transmission mode policy with preplanned UAV trajectory. A dynamic programming (DP) algorithm is proposed to obtain the optimal transmission policy. Then, with the fixed transmission policy, we optimize the UAV’s trajectory from the preplanned trace with recursive random search (RRS) algorithm. Numerical results show that the proposed scheme achieves significant energy savings gain over the benchmark schemes.

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Novel Energy-Efficient Data Gathering Scheme Exploiting Spatial-Temporal Correlation for Wireless Sensor Networks

Wireless Communications and Mobile Computing ◽

10.1155/2019/4182563 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Ying Zhou ◽

Lihua Yang ◽

Longxiang Yang ◽

Meng Ni

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Energy Efficient ◽

Data Gathering ◽

Temporal Correlation ◽

Wireless Sensor ◽

Selection Scheme ◽

Efficient Data ◽

Transmission Energy Consumption

A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal redundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy. Moreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application environments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink can recover measurements with reasonable accuracy by using the proposed method.

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Clustering and Compressive Data Gathering for Transmission Efficient Wireless Sensor Networks

Managing Resources for Futuristic Wireless Networks - Advances in Wireless Technologies and Telecommunication ◽

10.4018/978-1-5225-9493-2.ch002 ◽

2021 ◽

pp. 28-55

Author(s):

Utkarsha Sumedh Pacharaney ◽

Ranjan Bala Jain ◽

Rajiv Kumar Gupta

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Compressive Sensing ◽

Data Gathering ◽

Correlated Data ◽

Wireless Sensor ◽

Clustering Methods ◽

Compression Technique ◽

Spatially Correlated ◽

Sensory Data

The chapter focuses on minimizing the amount of wireless transmission in sensory data gathering for correlated data field monitoring in wireless sensor networks (WSN), which is a major source of power consumption. Compressive sensing (CS) is a new in-node compression technique that is economically used for data gathering in an energy-constrained WSN. Among existing CS-based routing, cluster-based methods offer the most transmission-efficient architecture. Most CS-based clustering methods randomly choose nodes to form clusters, neglecting the topology structure. A novel base station (BS)-assisted cluster, spatially correlated cluster using compressive sensing (SCC_CS), is proposed to reduce number of transmissions in and form the cluster by exploiting spatial correlation based on geographical proximity. The proposed BS-assisted clustering scheme follows hexagonal deployment strategy. In SCC_CS, cluster heads are solely involved in data gathering and transmitting CS measurements to BS, saving intra-cluster communication cost, and thus, network life increases as proved by simulation.

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